The number of filters in the Residual Block and the number of neurons of the hidden dense layer is 512. Batch normalization is used after hidden dense layers. The network is trained with cross-entropy loss and Adam optimizer with an initial learning rate of 0.001. Early stopping based on the validation accuracy is done, the training is stopped when the validation accuracy does not improve since 50 epochs. During training, we randomly do not update the weights of the visual path. The model is implemented in Tensorflow [1]}.
